Negative respiratory sinus arrhythmia (nRSA) in the MRI-scanner - a physiologic phenomenon observed during elevated anxiety in healthy persons

Scan-associated anxiety (scanxiety): the enigma of emotional breathing oscillations at 0.32 Hz (19 bpm)

MRI-related anxiety in healthy participants is often characterized by a dominant breathing frequency at around 0.32 Hz (19 breaths per minute, bpm) at the beginning but in a few cases also at the end of scanning. Breathing waves at 19 bpm are also observed in patients with anxiety independently of the scanned body part. In patients with medically intractable epilepsy and intracranial electroencephalography (iEEG), spontaneous breathing through the nose varied between 0.24 and 0.37 Hz (~19 bpm). Remarkable is the similarity of the observed breathing rates at around 0.32 Hz during different types of anxiety states (e.g., epilepsy, cancer, claustrophobia) with the preferred breathing frequency of 0.32 Hz (19 bpm), which is predicted by the binary hierarchy model of Klimesch. This elevated breathing frequency most likely reflects an emotional processing state, in which energy demands are minimized due to a harmonic coupling ratio with other brain-body oscillations.

Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population

In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with subject compliance, equipment failure or signal error. In large databases, such as the Human Connectome Projects, over half of the respiratory recordings may be unusable. As a result, the direct removal of low frequency respiratory variations from the blood oxygen level-dependent (BOLD) signal time series is not possible. This study proposes a deep learning-based method for reconstruction of respiratory variation (RV) waveforms directly from BOLD fMRI data in pediatric participants (aged 5 to 21 years old), and does not require any respiratory measurement device. To do this, the Lifespan Human Connectome Project in Development (HCP-D) dataset, which includes respiratory measurements, was used to both train a convolutional neural network (CNN) and evaluate its performance. Results show that a CNN can capture informative features from the BOLD signal time course and reconstruct accurate RV timeseries, especially when the subject has a prominent respiratory event. This work advances the use of direct estimation of physiological parameters from fMRI, which will eventually lead to reduced complexity and decrease the burden on participants because they may not be required to wear a respiratory bellows.

Analysis of Respiratory Sinus Arrhythmia and Directed Information Flow between Brain and Body Indicate Different Management Strategies of fMRI-Related Anxiety

Background: Respiratory sinus arrhythmia (RSA) denotes decrease of cardiac beat-to-beat intervals (RRI) during inspiration and RRI increase during expiration, but an inverse pattern (termed negative RSA) was also found in healthy humans with elevated anxiety. It was detected using wave-by-wave analysis of cardiorespiratory rhythms and was considered to reflect a strategy of anxiety management involving the activation of a neural pacemaker. Results were consistent with slow breathing, but contained uncertainty at normal breathing rates (0.2–0.4 Hz). Objectives and methods: We combined wave-by-wave analysis and directed information flow analysis to obtain information on anxiety management at higher breathing rates. We analyzed cardiorespiratory rhythms and blood oxygen level-dependent (BOLD) signals from the brainstem and cortex in 10 healthy fMRI participants with elevated anxiety. Results: Three subjects with slow respiratory, RRI, and neural BOLD oscillations showed 57 ± 26% negative RSA and significant anxiety reduction by 54 ± 9%. Six participants with breathing rate of ~0.3 Hz showed 41 ± 16% negative RSA and weaker anxiety reduction. They presented significant information flow from RRI to respiration and from the middle frontal cortex to the brainstem, which may result from respiration-entrained brain oscillations, indicating another anxiety management strategy. Conclusion: The two analytical approaches applied here indicate at least two different anxiety management strategies in healthy subjects.

Respiration-entrained brain oscillations in healthy fMRI participants with high anxiety

Brain-body interactions can be studied by using directed coupling measurements of fMRI oscillations in the low (0.1-0.2 Hz) and high frequency bands (HF; 0.2-0.4 Hz). Recently, a preponderance of oscillations in the information flow between the brainstem and the prefrontal cortex at around 0.15/0.16 Hz was shown. The goal of this study was to investigate the information flow between BOLD-, respiratory-, and heart beat-to-beat interval (RRI) signals in the HF band in healthy subjects with high anxiety during fMRI examinations. A multivariate autoregressive model was concurrently applied to the BOLD signals from the middle frontal gyrus (MFG), precentral gyrus and the brainstem, as well as to respiratory and RRI signals. Causal coupling between all signals was determined using the Directed Transfer Function (DTF). We found a salience of fast respiratory waves with a period of 3.1 s (corresponding to ~ 0.32 Hz) and a highly significant (p < 0.001) top-down information-flow from BOLD oscillations in the MFG to the brainstem. Additionally, there was a significant (p < 0.01) information flow from RRI to respiratory oscillations. We speculate that brain oscillations around 0.32 Hz, triggered by nasal breathing, are projected downwards to the brainstem. Particularly interesting is the driving force of cardiac to respiratory waves with a ratio of 1:1 or 1:2. These results support the binary hierarchy model with preferred respiratory frequencies at 0.32 Hz and 0.16 Hz.

Processing of fMRI-related anxiety and information flow between brain and body revealed a preponderance of oscillations at 0.15/0.16 Hz

Slow oscillations of different center frequencies and their coupling play an important role in brain-body interactions. The crucial question analyzed by us is, whether the low frequency (LF) band (0.05-0.15 Hz) or the intermediate frequency (IMF) band (0.1-0.2 Hz) is more eminent in respect of the information flow between body (heart rate and respiration) and BOLD signals in cortex and brainstem. A recently published study with the LF band in fMRI-naïve subjects revealed an intensive information flow from the cortex to the brainstem and a weaker flow from the brainstem to the cortex. The comparison of both bands revealed a significant information flow from the middle frontal gyrus (MFG) to the precentral gyrus (PCG) and from brainstem to PCG only in the IMF band. This pattern of directed coupling between slow oscillations in the cortex and brainstem not only supports the existence of a pacemaker-like structure in brainstem, but provides first evidence that oscillations centered at 0.15/0.16 Hz can also emerge in brain networks. BOLD oscillations in resting states are dominating at ~ 0.08 Hz and respiratory rates at ~ 0.32 Hz. Therefore, the frequency component at ~ 0.16 Hz (doubling-halving 0.08 Hz or 0.32 Hz) is of special interest, because phase coupled oscillations can reduce the energy demand.